Work

Measuring Diversity of Game Scenarios

Year: 2025

Type: article

Abstract: This paper comprehensively reviews the metrics for measuring the diversity of game scenarios, spotlighting the innovative use of procedural content generation and other fields as cornerstones for enriching player experiences through diverse game scenarios. By traversing a wide array of disciplines, from affective modeling and multiagent systems to psychological studies, our research underscores the importance of diverse game scenarios in gameplay and education. Through a taxonomy of diversity metrics and evaluation methods, we aim to bridge the current gaps in literature and practice, offering insights into effective strategies for measuring and integrating diversity in game scenarios. Our analysis highlights the necessity for a unified taxonomy to aid developers and researchers in crafting more engaging and varied game worlds. This survey not only charts a path for future research in diverse game scenarios but also serves as a handbook for industry practitioners seeking to leverage diversity as a key component of game design and development.

Source: IEEE Transactions on Games

DOI: 10.1109/tg.2025.3543135

Authors: Yuchen Li, Ziqi Wang, Qingquan Zhang, Bo Yuan, Jialin Liu

Institutions: New York University, Hong Kong University of Science and Technology, Southern University of Science and Technology, Lingnan University

Language: English

Topic: Artificial Intelligence in Games

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Lingnan University

Awards: G8555519613, G8710702756, G966106430